Description: Third article in a series on how to use predictive analytics to improve the development of long-term care projection assumptions.

Description: This article describes methodologies for detection and deterrence of Medicare fraud. It also describes the validation of PRIDIT for fraud detection in Medicare.

Description: It talks about the major areas of data science and how to learn data science with the actuarial background.

Description: In this article, we will overview some popular tree-based models and understand how such models may be applied to the insurance industry.

Description: Defines Accelerated Underwriting programs as a transformational trend, and outlines a process for evaluating mortality implications of such programs.

Description: The well-known examples of predictive modeling “successes” are numerous. Perhaps less widely recognized is the myriad of ways in which a predictive model can fail to perform as expected, often due to misconceptions or misrepresentations on the part of the analyst. This article, which is intended for a general actuarial audience, focuses on three such pitfalls. By understanding these three common pitfalls, readers may guard against them within their own work, teams, and organizations.

Description: Second article in a series on how to use predictive analytics to improve the development of long-term care projection assumptions.

Description: Even the best algorithms are only effective if they are trained on appropriate data. This article discusses common pitfalls in creating a training data set.

Description: Overview of regularization and least angle regression approaches.

Description: First article in a series that walks through the progression from developing LTC assumptions using traditional methods to doing so using predictive analytics.